Research on the Tactic of Intelligent Test-Paper Generation Based on PSO Algorithm

阎峰; 安晓东

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Research on the Tactic of Intelligent Test-Paper Generation Based on PSO Algorithm

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- Research on the Tactic of Intelligent Test-Paper Generation Based on PSO Algorithm
- 基于粒子群优化算法的智能抽题策略研究
- Issue 4, Pages: 333-337(2008)
- 作者机构：
  
  1. 山西省人事考试中心
  2. 山西人事信息中心
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2008
- 稿件说明：
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阎峰, 安晓东. Research on the Tactic of Intelligent Test-Paper Generation Based on PSO Algorithm[J]. 2008, (4): 333-337.
DOI：

阎峰, 安晓东. Research on the Tactic of Intelligent Test-Paper Generation Based on PSO Algorithm[J]. 2008, (4): 333-337. DOI：

摘要

通过对智能抽题策略研究

建立了智能抽题数学模型

提出用于评价抽题效果的评价指标及其获取方法;在此基础上

考虑智能组卷过程对试卷质量的高要求以及优化参数的表示形式

文中将粒子群优化算法运用于智能抽题优化模型的求解中

取得了较好的效果.为证明该方法的有效性和优越性

与采用相同二进制编码方式的遗传算法进行比较

通过二者的适应度演变图可以表明粒子群算法(Particle SwarmOptimization

PSO)算法可以较早地达到目标的最优分配

在运行时间及抽卷优化结果上明显优于遗传算法(Genetic Algorithm

GA)

能更好地适应智能抽题策略的要求.

Abstract

By studying the tactic of intelligent test-paper generation

the paper builds its mathematic model and puts forward the evaluation index and quantitative method for evaluating the effects of taking test questions.On the basis of it

computational algorithms for optimal design of the model of taking test questions are implemented by means of improved particle swarm optimization(PSO).In order to show the effectiveness and superiority of this method

PSO algorithm is compared with genetic algorithm(GA) algorithm by adaptation evolvement chart of test-paper generation with the same binary encoding method.The result indicates that the PSO algorithm can achieve the superior object more quickly

the running time and optimization result of PSO surpass that of GA obviously.The method can satisfy well the strategy request of intelligent test paper generation.

关键词

Keywords

references

吴水秀.智能试卷生成和自适应考试系统[J].计算机与现代化,1999(1):36-39.

王清辉.基于随机数的计算机智能组卷算法探索[J].福建电脑,2006(11):125.

唐朝舜,董玉德,熊蓉.在线随机组卷算法研究及实现[J].合肥工业大学学报(自然科学版),2006,29(3):296-299.Tang Chaoshun,Dong Yude,Xiong Rong.Algorithm for random creating test papers online and its realization[J].Journal of Hefei University of Technology,2006,29(3):296-299.(in Chinese)

田民格,卢昌荆,陈秀琼.遗传算法在无纸化考试系统中实现随机抽题[J].三明学院学报,2007,24(2):200-203.Tian Minge,Lu Changjing,Chen Xiuqiong.Genetic algorithm realizes selecting randomly questions of No paperexamination system[J].Journal of Sanming Universtiy,2007,24(2):200-203.(in Chinese)

张爱文,樊红莲.自适应遗传算法用于自动组卷中的数学模型设计[J].哈尔滨理工大学学报,2006,11(5):18-20Zhang Aiwen,Fan Honglian.Application of an adaptive genetic algorithm in composing test papers[J].JournalHarbinun iv.SCI.&TECH,2006,11(5):18-20.(in Chinese)

An Xiaodong,Yan Jinglong,Shi Dexing,et al.A tactic of intelligent buildup paper for test paper based on geneticalgorithm[C].The 6th Internaional Symposium on Test and Measurement,Dalian:2005.

Kennedy J.Particle swarm optimization[C].IEEE International Conference on Neural Networks,1995:1942.

Eberthart R C,Shi Y.Particle swarm optimization:developments,applications and resources[C].Proceedings ofthe IEEE Congress on Evolutionary Computation.Piscataway,2001:81-86.

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